Overview

Dataset statistics

Number of variables22
Number of observations62554
Missing cells164374
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 MiB
Average record size in memory169.0 B

Variable types

Categorical13
Numeric8
Boolean1

Alerts

year has constant value "2020"Constant
dt_kst has a high cardinality: 39340 distinct valuesHigh cardinality
actionOption is highly overall correlated with action and 3 other fieldsHigh correlation
actionSubOption is highly overall correlated with action and 3 other fieldsHigh correlation
emotionPositive is highly overall correlated with user_idHigh correlation
emotionTension is highly overall correlated with user_idHigh correlation
activity is highly overall correlated with actionSubHigh correlation
day is highly overall correlated with date_ymd and 2 other fieldsHigh correlation
day_of_week is highly overall correlated with date_ymd and 2 other fieldsHigh correlation
hour is highly overall correlated with actionSub and 1 other fieldsHigh correlation
action is highly overall correlated with actionOption and 5 other fieldsHigh correlation
actionSub is highly overall correlated with actionOption and 6 other fieldsHigh correlation
condition is highly overall correlated with actionOption and 4 other fieldsHigh correlation
conditionSub1Option is highly overall correlated with action and 4 other fieldsHigh correlation
conditionSub2Option is highly overall correlated with actionSubOption and 1 other fieldsHigh correlation
place is highly overall correlated with actionOption and 4 other fieldsHigh correlation
date_ymd is highly overall correlated with day and 5 other fieldsHigh correlation
month is highly overall correlated with day and 1 other fieldsHigh correlation
day_name is highly overall correlated with day_of_week and 2 other fieldsHigh correlation
meridiem is highly overall correlated with hourHigh correlation
is_weekend is highly overall correlated with day and 4 other fieldsHigh correlation
user_id is highly overall correlated with actionSubOption and 3 other fieldsHigh correlation
month is highly imbalanced (72.6%)Imbalance
actionSub has 47876 (76.5%) missing valuesMissing
actionSubOption has 47876 (76.5%) missing valuesMissing
conditionSub1Option has 34311 (54.9%) missing valuesMissing
conditionSub2Option has 34311 (54.9%) missing valuesMissing
dt_kst is uniformly distributedUniform
activity has 3050 (4.9%) zerosZeros
day_of_week has 8712 (13.9%) zerosZeros
hour has 1368 (2.2%) zerosZeros

Reproduction

Analysis started2023-03-28 11:05:41.462833
Analysis finished2023-03-28 11:05:54.864032
Duration13.4 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

action
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
sleep
14722 
work
13770 
recreation_media
9327 
travel
8455 
meal
6223 
Other values (8)
10057 

Length

Max length16
Median length14
Mean length7.599562
Min length4

Characters and Unicode

Total characters475383
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsleep
2nd rowsleep
3rd rowsleep
4th rowsleep
5th rowsleep

Common Values

ValueCountFrequency (%)
sleep 14722
23.5%
work 13770
22.0%
recreation_media 9327
14.9%
travel 8455
13.5%
meal 6223
9.9%
recreation_etc 3552
 
5.7%
personal_care 3221
 
5.1%
household 848
 
1.4%
shop 762
 
1.2%
socialising 720
 
1.2%
Other values (3) 954
 
1.5%

Length

2023-03-28T20:05:54.927255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sleep 14722
23.5%
work 13770
22.0%
recreation_media 9327
14.9%
travel 8455
13.5%
meal 6223
9.9%
recreation_etc 3552
 
5.7%
personal_care 3221
 
5.1%
household 848
 
1.4%
shop 762
 
1.2%
socialising 720
 
1.2%
Other values (3) 954
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 92035
19.4%
r 55349
11.6%
a 44970
9.5%
l 34189
 
7.2%
o 33864
 
7.1%
t 27396
 
5.8%
i 25028
 
5.3%
s 20993
 
4.4%
c 20634
 
4.3%
n 18806
 
4.0%
Other values (12) 102119
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 459021
96.6%
Connector Punctuation 16362
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 92035
20.1%
r 55349
12.1%
a 44970
9.8%
l 34189
 
7.4%
o 33864
 
7.4%
t 27396
 
6.0%
i 25028
 
5.5%
s 20993
 
4.6%
c 20634
 
4.5%
n 18806
 
4.1%
Other values (11) 85757
18.7%
Connector Punctuation
ValueCountFrequency (%)
_ 16362
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 459021
96.6%
Common 16362
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 92035
20.1%
r 55349
12.1%
a 44970
9.8%
l 34189
 
7.4%
o 33864
 
7.4%
t 27396
 
6.0%
i 25028
 
5.5%
s 20993
 
4.6%
c 20634
 
4.5%
n 18806
 
4.1%
Other values (11) 85757
18.7%
Common
ValueCountFrequency (%)
_ 16362
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 92035
19.4%
r 55349
11.6%
a 44970
9.5%
l 34189
 
7.2%
o 33864
 
7.1%
t 27396
 
5.8%
i 25028
 
5.3%
s 20993
 
4.4%
c 20634
 
4.3%
n 18806
 
4.0%
Other values (12) 102119
21.5%

actionOption
Real number (ℝ)

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.75506
Minimum41
Maximum793
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.8 KiB
2023-03-28T20:05:55.030003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile82
Q1111
median132
Q3211
95-th percentile793
Maximum793
Range752
Interquartile range (IQR)100

Descriptive statistics

Standard deviation259.77467
Coefficient of variation (CV)0.93864469
Kurtosis-0.45239509
Mean276.75506
Median Absolute Deviation (MAD)50
Skewness1.1798849
Sum17312136
Variance67482.877
MonotonicityNot monotonic
2023-03-28T20:05:55.125914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
111 14491
23.2%
211 13533
21.6%
722 5652
 
9.0%
82 5625
 
9.0%
121 5538
 
8.9%
793 3250
 
5.2%
132 2258
 
3.6%
724 1899
 
3.0%
725 1628
 
2.6%
87 1462
 
2.3%
Other values (23) 7218
11.5%
ValueCountFrequency (%)
41 40
 
0.1%
43 134
 
0.2%
45 290
 
0.5%
46 384
 
0.6%
81 1145
 
1.8%
82 5625
9.0%
84 223
 
0.4%
87 1462
 
2.3%
91 58
 
0.1%
92 704
 
1.1%
ValueCountFrequency (%)
793 3250
5.2%
791 302
 
0.5%
761 30
 
< 0.1%
756 2
 
< 0.1%
751 260
 
0.4%
746 502
 
0.8%
742 160
 
0.3%
725 1628
 
2.6%
724 1899
 
3.0%
722 5652
9.0%

actionSub
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing47876
Missing (%)76.5%
Memory size488.8 KiB
move_method
8455 
meal_amount
6223 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters161458
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmeal_amount
2nd rowmeal_amount
3rd rowmeal_amount
4th rowmeal_amount
5th rowmeal_amount

Common Values

ValueCountFrequency (%)
move_method 8455
 
13.5%
meal_amount 6223
 
9.9%
(Missing) 47876
76.5%

Length

2023-03-28T20:05:55.225197image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:55.327768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
move_method 8455
57.6%
meal_amount 6223
42.4%

Most occurring characters

ValueCountFrequency (%)
m 29356
18.2%
o 23133
14.3%
e 23133
14.3%
_ 14678
9.1%
t 14678
9.1%
a 12446
7.7%
v 8455
 
5.2%
h 8455
 
5.2%
d 8455
 
5.2%
l 6223
 
3.9%
Other values (2) 12446
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 146780
90.9%
Connector Punctuation 14678
 
9.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 29356
20.0%
o 23133
15.8%
e 23133
15.8%
t 14678
10.0%
a 12446
8.5%
v 8455
 
5.8%
h 8455
 
5.8%
d 8455
 
5.8%
l 6223
 
4.2%
u 6223
 
4.2%
Connector Punctuation
ValueCountFrequency (%)
_ 14678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 146780
90.9%
Common 14678
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 29356
20.0%
o 23133
15.8%
e 23133
15.8%
t 14678
10.0%
a 12446
8.5%
v 8455
 
5.8%
h 8455
 
5.8%
d 8455
 
5.8%
l 6223
 
4.2%
u 6223
 
4.2%
Common
ValueCountFrequency (%)
_ 14678
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 161458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 29356
18.2%
o 23133
14.3%
e 23133
14.3%
_ 14678
9.1%
t 14678
9.1%
a 12446
7.7%
v 8455
 
5.2%
h 8455
 
5.2%
d 8455
 
5.2%
l 6223
 
3.9%
Other values (2) 12446
7.7%

actionSubOption
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)< 0.1%
Missing47876
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean3.2885952
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.8 KiB
2023-03-28T20:05:55.398212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9945677
Coefficient of variation (CV)0.60651054
Kurtosis-1.5053757
Mean3.2885952
Median Absolute Deviation (MAD)2
Skewness0.35005424
Sum48270
Variance3.9783001
MonotonicityNot monotonic
2023-03-28T20:05:55.466080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 4170
 
6.7%
1 3507
 
5.6%
2 3418
 
5.5%
3 2544
 
4.1%
5 999
 
1.6%
7 40
 
0.1%
(Missing) 47876
76.5%
ValueCountFrequency (%)
1 3507
5.6%
2 3418
5.5%
3 2544
4.1%
5 999
 
1.6%
6 4170
6.7%
7 40
 
0.1%
ValueCountFrequency (%)
7 40
 
0.1%
6 4170
6.7%
5 999
 
1.6%
3 2544
4.1%
2 3418
5.5%
1 3507
5.6%

condition
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
ALONE
34450 
WITH_MANY
15888 
WITH_ONE
12216 

Length

Max length9
Median length5
Mean length6.601816
Min length5

Characters and Unicode

Total characters412970
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWITH_ONE
2nd rowWITH_ONE
3rd rowWITH_ONE
4th rowWITH_ONE
5th rowWITH_ONE

Common Values

ValueCountFrequency (%)
ALONE 34450
55.1%
WITH_MANY 15888
25.4%
WITH_ONE 12216
 
19.5%

Length

2023-03-28T20:05:55.559061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:55.660713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
alone 34450
55.1%
with_many 15888
25.4%
with_one 12216
 
19.5%

Most occurring characters

ValueCountFrequency (%)
N 62554
15.1%
A 50338
12.2%
O 46666
11.3%
E 46666
11.3%
L 34450
8.3%
W 28104
6.8%
I 28104
6.8%
T 28104
6.8%
H 28104
6.8%
_ 28104
6.8%
Other values (2) 31776
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 384866
93.2%
Connector Punctuation 28104
 
6.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 62554
16.3%
A 50338
13.1%
O 46666
12.1%
E 46666
12.1%
L 34450
9.0%
W 28104
7.3%
I 28104
7.3%
T 28104
7.3%
H 28104
7.3%
M 15888
 
4.1%
Connector Punctuation
ValueCountFrequency (%)
_ 28104
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 384866
93.2%
Common 28104
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 62554
16.3%
A 50338
13.1%
O 46666
12.1%
E 46666
12.1%
L 34450
9.0%
W 28104
7.3%
I 28104
7.3%
T 28104
7.3%
H 28104
7.3%
M 15888
 
4.1%
Common
ValueCountFrequency (%)
_ 28104
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 412970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 62554
15.1%
A 50338
12.2%
O 46666
11.3%
E 46666
11.3%
L 34450
8.3%
W 28104
6.8%
I 28104
6.8%
T 28104
6.8%
H 28104
6.8%
_ 28104
6.8%
Other values (2) 31776
7.7%

conditionSub1Option
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing34311
Missing (%)54.9%
Memory size488.8 KiB
3.0
14984 
2.0
10110 
1.0
2706 
5.0
 
300
4.0
 
143

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84729
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 14984
24.0%
2.0 10110
 
16.2%
1.0 2706
 
4.3%
5.0 300
 
0.5%
4.0 143
 
0.2%
(Missing) 34311
54.9%

Length

2023-03-28T20:05:55.744355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:55.836541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3.0 14984
53.1%
2.0 10110
35.8%
1.0 2706
 
9.6%
5.0 300
 
1.1%
4.0 143
 
0.5%

Most occurring characters

ValueCountFrequency (%)
. 28243
33.3%
0 28243
33.3%
3 14984
17.7%
2 10110
 
11.9%
1 2706
 
3.2%
5 300
 
0.4%
4 143
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56486
66.7%
Other Punctuation 28243
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28243
50.0%
3 14984
26.5%
2 10110
 
17.9%
1 2706
 
4.8%
5 300
 
0.5%
4 143
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 28243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84729
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 28243
33.3%
0 28243
33.3%
3 14984
17.7%
2 10110
 
11.9%
1 2706
 
3.2%
5 300
 
0.4%
4 143
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 28243
33.3%
0 28243
33.3%
3 14984
17.7%
2 10110
 
11.9%
1 2706
 
3.2%
5 300
 
0.4%
4 143
 
0.2%

conditionSub2Option
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing34311
Missing (%)54.9%
Memory size488.8 KiB
1.0
17100 
2.0
8526 
3.0
2617 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84729
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 17100
27.3%
2.0 8526
 
13.6%
3.0 2617
 
4.2%
(Missing) 34311
54.9%

Length

2023-03-28T20:05:55.921685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:56.008532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 17100
60.5%
2.0 8526
30.2%
3.0 2617
 
9.3%

Most occurring characters

ValueCountFrequency (%)
. 28243
33.3%
0 28243
33.3%
1 17100
20.2%
2 8526
 
10.1%
3 2617
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56486
66.7%
Other Punctuation 28243
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28243
50.0%
1 17100
30.3%
2 8526
 
15.1%
3 2617
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 28243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84729
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 28243
33.3%
0 28243
33.3%
1 17100
20.2%
2 8526
 
10.1%
3 2617
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 28243
33.3%
0 28243
33.3%
1 17100
20.2%
2 8526
 
10.1%
3 2617
 
3.1%

place
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
home
27500 
workplace
14749 
outdoor
10228 
other_indoor
7795 
restaurant
 
2282

Length

Max length12
Median length10
Mean length6.8852032
Min length4

Characters and Unicode

Total characters430697
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother_indoor
2nd rowother_indoor
3rd rowother_indoor
4th rowother_indoor
5th rowother_indoor

Common Values

ValueCountFrequency (%)
home 27500
44.0%
workplace 14749
23.6%
outdoor 10228
 
16.4%
other_indoor 7795
 
12.5%
restaurant 2282
 
3.6%

Length

2023-03-28T20:05:56.094480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:56.198452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
home 27500
44.0%
workplace 14749
23.6%
outdoor 10228
 
16.4%
other_indoor 7795
 
12.5%
restaurant 2282
 
3.6%

Most occurring characters

ValueCountFrequency (%)
o 96318
22.4%
e 52326
12.1%
r 45131
10.5%
h 35295
 
8.2%
m 27500
 
6.4%
t 22587
 
5.2%
a 19313
 
4.5%
d 18023
 
4.2%
p 14749
 
3.4%
l 14749
 
3.4%
Other values (8) 84706
19.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 422902
98.2%
Connector Punctuation 7795
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 96318
22.8%
e 52326
12.4%
r 45131
10.7%
h 35295
 
8.3%
m 27500
 
6.5%
t 22587
 
5.3%
a 19313
 
4.6%
d 18023
 
4.3%
p 14749
 
3.5%
l 14749
 
3.5%
Other values (7) 76911
18.2%
Connector Punctuation
ValueCountFrequency (%)
_ 7795
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 422902
98.2%
Common 7795
 
1.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 96318
22.8%
e 52326
12.4%
r 45131
10.7%
h 35295
 
8.3%
m 27500
 
6.5%
t 22587
 
5.3%
a 19313
 
4.6%
d 18023
 
4.3%
p 14749
 
3.5%
l 14749
 
3.5%
Other values (7) 76911
18.2%
Common
ValueCountFrequency (%)
_ 7795
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 96318
22.4%
e 52326
12.1%
r 45131
10.5%
h 35295
 
8.2%
m 27500
 
6.4%
t 22587
 
5.2%
a 19313
 
4.5%
d 18023
 
4.2%
p 14749
 
3.4%
l 14749
 
3.4%
Other values (8) 84706
19.7%

emotionPositive
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9777792
Minimum2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.8 KiB
2023-03-28T20:05:56.282466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14
median5
Q35
95-th percentile7
Maximum7
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90969229
Coefficient of variation (CV)0.18275063
Kurtosis0.43750453
Mean4.9777792
Median Absolute Deviation (MAD)0
Skewness0.34410448
Sum311380
Variance0.82754006
MonotonicityNot monotonic
2023-03-28T20:05:56.361107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 33769
54.0%
4 13786
22.0%
6 7417
 
11.9%
7 5049
 
8.1%
3 2480
 
4.0%
2 53
 
0.1%
ValueCountFrequency (%)
2 53
 
0.1%
3 2480
 
4.0%
4 13786
22.0%
5 33769
54.0%
6 7417
 
11.9%
7 5049
 
8.1%
ValueCountFrequency (%)
7 5049
 
8.1%
6 7417
 
11.9%
5 33769
54.0%
4 13786
22.0%
3 2480
 
4.0%
2 53
 
0.1%

emotionTension
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0129168
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.8 KiB
2023-03-28T20:05:56.437538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile4
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94734996
Coefficient of variation (CV)0.47063542
Kurtosis0.93237052
Mean2.0129168
Median Absolute Deviation (MAD)1
Skewness1.0693149
Sum125916
Variance0.89747195
MonotonicityNot monotonic
2023-03-28T20:05:56.515144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 30999
49.6%
1 19107
30.5%
3 6033
 
9.6%
4 5408
 
8.6%
5 962
 
1.5%
6 45
 
0.1%
ValueCountFrequency (%)
1 19107
30.5%
2 30999
49.6%
3 6033
 
9.6%
4 5408
 
8.6%
5 962
 
1.5%
6 45
 
0.1%
ValueCountFrequency (%)
6 45
 
0.1%
5 962
 
1.5%
4 5408
 
8.6%
3 6033
 
9.6%
2 30999
49.6%
1 19107
30.5%

activity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9145378
Minimum0
Maximum6
Zeros3050
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size488.8 KiB
2023-03-28T20:05:56.592493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75680131
Coefficient of variation (CV)0.25966426
Kurtosis8.349256
Mean2.9145378
Median Absolute Deviation (MAD)0
Skewness-2.5428023
Sum182316
Variance0.57274822
MonotonicityNot monotonic
2023-03-28T20:05:56.663226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 50961
81.5%
4 6142
 
9.8%
0 3050
 
4.9%
2 2344
 
3.7%
1 33
 
0.1%
6 24
 
< 0.1%
ValueCountFrequency (%)
0 3050
 
4.9%
1 33
 
0.1%
2 2344
 
3.7%
3 50961
81.5%
4 6142
 
9.8%
6 24
 
< 0.1%
ValueCountFrequency (%)
6 24
 
< 0.1%
4 6142
 
9.8%
3 50961
81.5%
2 2344
 
3.7%
1 33
 
0.1%
0 3050
 
4.9%

dt_kst
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct39340
Distinct (%)62.9%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
2020-09-03 13:25:00+09:00
 
4
2020-09-02 13:30:00+09:00
 
4
2020-09-20 21:19:00+09:00
 
3
2020-09-03 09:17:00+09:00
 
3
2020-09-20 18:14:00+09:00
 
3
Other values (39335)
62537 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters1563850
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16311 ?
Unique (%)26.1%

Sample

1st row2020-08-30 00:00:00+09:00
2nd row2020-08-30 00:01:00+09:00
3rd row2020-08-30 00:02:00+09:00
4th row2020-08-30 00:03:00+09:00
5th row2020-08-30 00:04:00+09:00

Common Values

ValueCountFrequency (%)
2020-09-03 13:25:00+09:00 4
 
< 0.1%
2020-09-02 13:30:00+09:00 4
 
< 0.1%
2020-09-20 21:19:00+09:00 3
 
< 0.1%
2020-09-03 09:17:00+09:00 3
 
< 0.1%
2020-09-20 18:14:00+09:00 3
 
< 0.1%
2020-09-16 12:40:00+09:00 3
 
< 0.1%
2020-09-14 17:40:00+09:00 3
 
< 0.1%
2020-09-25 11:33:00+09:00 3
 
< 0.1%
2020-09-20 18:07:00+09:00 3
 
< 0.1%
2020-09-16 09:10:00+09:00 3
 
< 0.1%
Other values (39330) 62522
99.9%

Length

2023-03-28T20:05:56.751120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-09-17 2451
 
2.0%
2020-09-15 2451
 
2.0%
2020-09-09 2414
 
1.9%
2020-09-24 2389
 
1.9%
2020-09-22 2384
 
1.9%
2020-09-14 2380
 
1.9%
2020-09-10 2367
 
1.9%
2020-09-07 2364
 
1.9%
2020-09-11 2353
 
1.9%
2020-09-02 2333
 
1.9%
Other values (1459) 101222
80.9%

Most occurring characters

ValueCountFrequency (%)
0 569028
36.4%
: 187662
 
12.0%
2 185138
 
11.8%
9 139292
 
8.9%
- 125108
 
8.0%
1 87092
 
5.6%
62554
 
4.0%
+ 62554
 
4.0%
3 33040
 
2.1%
5 28194
 
1.8%
Other values (4) 84188
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1125972
72.0%
Other Punctuation 187662
 
12.0%
Dash Punctuation 125108
 
8.0%
Space Separator 62554
 
4.0%
Math Symbol 62554
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 569028
50.5%
2 185138
 
16.4%
9 139292
 
12.4%
1 87092
 
7.7%
3 33040
 
2.9%
5 28194
 
2.5%
4 28128
 
2.5%
8 19692
 
1.7%
7 18828
 
1.7%
6 17540
 
1.6%
Other Punctuation
ValueCountFrequency (%)
: 187662
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 125108
100.0%
Space Separator
ValueCountFrequency (%)
62554
100.0%
Math Symbol
ValueCountFrequency (%)
+ 62554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1563850
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 569028
36.4%
: 187662
 
12.0%
2 185138
 
11.8%
9 139292
 
8.9%
- 125108
 
8.0%
1 87092
 
5.6%
62554
 
4.0%
+ 62554
 
4.0%
3 33040
 
2.1%
5 28194
 
1.8%
Other values (4) 84188
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1563850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 569028
36.4%
: 187662
 
12.0%
2 185138
 
11.8%
9 139292
 
8.9%
- 125108
 
8.0%
1 87092
 
5.6%
62554
 
4.0%
+ 62554
 
4.0%
3 33040
 
2.1%
5 28194
 
1.8%
Other values (4) 84188
 
5.4%

date_ymd
Categorical

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
2020-09-15
 
2451
2020-09-17
 
2451
2020-09-09
 
2414
2020-09-24
 
2389
2020-09-22
 
2384
Other values (24)
50465 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters625540
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-08-30
2nd row2020-08-30
3rd row2020-08-30
4th row2020-08-30
5th row2020-08-30

Common Values

ValueCountFrequency (%)
2020-09-15 2451
 
3.9%
2020-09-17 2451
 
3.9%
2020-09-09 2414
 
3.9%
2020-09-24 2389
 
3.8%
2020-09-22 2384
 
3.8%
2020-09-14 2380
 
3.8%
2020-09-10 2367
 
3.8%
2020-09-07 2364
 
3.8%
2020-09-11 2353
 
3.8%
2020-09-02 2333
 
3.7%
Other values (19) 38668
61.8%

Length

2023-03-28T20:05:56.837328image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-09-15 2451
 
3.9%
2020-09-17 2451
 
3.9%
2020-09-09 2414
 
3.9%
2020-09-24 2389
 
3.8%
2020-09-22 2384
 
3.8%
2020-09-14 2380
 
3.8%
2020-09-10 2367
 
3.8%
2020-09-07 2364
 
3.8%
2020-09-11 2353
 
3.8%
2020-09-02 2333
 
3.7%
Other values (19) 38668
61.8%

Most occurring characters

ValueCountFrequency (%)
0 212448
34.0%
2 149836
24.0%
- 125108
20.0%
9 64224
 
10.3%
1 30774
 
4.9%
3 9195
 
1.5%
8 7181
 
1.1%
7 6968
 
1.1%
5 6832
 
1.1%
4 6643
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500432
80.0%
Dash Punctuation 125108
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 212448
42.5%
2 149836
29.9%
9 64224
 
12.8%
1 30774
 
6.1%
3 9195
 
1.8%
8 7181
 
1.4%
7 6968
 
1.4%
5 6832
 
1.4%
4 6643
 
1.3%
6 6331
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 125108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 625540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 212448
34.0%
2 149836
24.0%
- 125108
20.0%
9 64224
 
10.3%
1 30774
 
4.9%
3 9195
 
1.5%
8 7181
 
1.1%
7 6968
 
1.1%
5 6832
 
1.1%
4 6643
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 625540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 212448
34.0%
2 149836
24.0%
- 125108
20.0%
9 64224
 
10.3%
1 30774
 
4.9%
3 9195
 
1.5%
8 7181
 
1.1%
7 6968
 
1.1%
5 6832
 
1.1%
4 6643
 
1.1%

year
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
2020
62554 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters250216
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 62554
100.0%

Length

2023-03-28T20:05:56.922607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:57.006820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2020 62554
100.0%

Most occurring characters

ValueCountFrequency (%)
2 125108
50.0%
0 125108
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 250216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 125108
50.0%
0 125108
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 250216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 125108
50.0%
0 125108
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 125108
50.0%
0 125108
50.0%

month
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
9
59607 
8
 
2947

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters62554
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8
2nd row8
3rd row8
4th row8
5th row8

Common Values

ValueCountFrequency (%)
9 59607
95.3%
8 2947
 
4.7%

Length

2023-03-28T20:05:57.077293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:57.164231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
9 59607
95.3%
8 2947
 
4.7%

Most occurring characters

ValueCountFrequency (%)
9 59607
95.3%
8 2947
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62554
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 59607
95.3%
8 2947
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 62554
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9 59607
95.3%
8 2947
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 59607
95.3%
8 2947
 
4.7%

day
Real number (ℝ)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.962672
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size488.8 KiB
2023-03-28T20:05:57.369032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q322
95-th percentile27
Maximum31
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.3223813
Coefficient of variation (CV)0.55620956
Kurtosis-1.0662622
Mean14.962672
Median Absolute Deviation (MAD)7
Skewness0.05465845
Sum935975
Variance69.262031
MonotonicityNot monotonic
2023-03-28T20:05:57.459396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
15 2451
 
3.9%
17 2451
 
3.9%
9 2414
 
3.9%
24 2389
 
3.8%
22 2384
 
3.8%
14 2380
 
3.8%
10 2367
 
3.8%
7 2364
 
3.8%
11 2353
 
3.8%
2 2333
 
3.7%
Other values (19) 38668
61.8%
ValueCountFrequency (%)
1 1969
3.1%
2 2333
3.7%
3 2255
3.6%
4 1874
3.0%
5 2078
3.3%
6 1957
3.1%
7 2364
3.8%
8 1917
3.1%
9 2414
3.9%
10 2367
3.8%
ValueCountFrequency (%)
31 1700
2.7%
30 1247
2.0%
27 2153
3.4%
26 2217
3.5%
25 2303
3.7%
24 2389
3.8%
23 2237
3.6%
22 2384
3.8%
21 2268
3.6%
20 2011
3.2%

day_of_week
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0094958
Minimum0
Maximum6
Zeros8712
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size488.8 KiB
2023-03-28T20:05:57.546427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9894465
Coefficient of variation (CV)0.66105642
Kurtosis-1.2255216
Mean3.0094958
Median Absolute Deviation (MAD)2
Skewness0.0048425372
Sum188256
Variance3.9578975
MonotonicityNot monotonic
2023-03-28T20:05:57.617210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 9462
15.1%
2 9141
14.6%
6 9124
14.6%
4 8847
14.1%
1 8721
13.9%
0 8712
13.9%
5 8547
13.7%
ValueCountFrequency (%)
0 8712
13.9%
1 8721
13.9%
2 9141
14.6%
3 9462
15.1%
4 8847
14.1%
5 8547
13.7%
6 9124
14.6%
ValueCountFrequency (%)
6 9124
14.6%
5 8547
13.7%
4 8847
14.1%
3 9462
15.1%
2 9141
14.6%
1 8721
13.9%
0 8712
13.9%

day_name
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
Thursday
9462 
Wednesday
9141 
Sunday
9124 
Friday
8847 
Tuesday
8721 
Other values (2)
17259 

Length

Max length9
Median length8
Mean length7.1535953
Min length6

Characters and Unicode

Total characters447486
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunday
2nd rowSunday
3rd rowSunday
4th rowSunday
5th rowSunday

Common Values

ValueCountFrequency (%)
Thursday 9462
15.1%
Wednesday 9141
14.6%
Sunday 9124
14.6%
Friday 8847
14.1%
Tuesday 8721
13.9%
Monday 8712
13.9%
Saturday 8547
13.7%

Length

2023-03-28T20:05:57.706255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:57.815119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
thursday 9462
15.1%
wednesday 9141
14.6%
sunday 9124
14.6%
friday 8847
14.1%
tuesday 8721
13.9%
monday 8712
13.9%
saturday 8547
13.7%

Most occurring characters

ValueCountFrequency (%)
d 71695
16.0%
a 71101
15.9%
y 62554
14.0%
u 35854
8.0%
s 27324
 
6.1%
e 27003
 
6.0%
n 26977
 
6.0%
r 26856
 
6.0%
T 18183
 
4.1%
S 17671
 
3.9%
Other values (7) 62268
13.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 384932
86.0%
Uppercase Letter 62554
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 71695
18.6%
a 71101
18.5%
y 62554
16.3%
u 35854
9.3%
s 27324
 
7.1%
e 27003
 
7.0%
n 26977
 
7.0%
r 26856
 
7.0%
h 9462
 
2.5%
i 8847
 
2.3%
Other values (2) 17259
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
T 18183
29.1%
S 17671
28.2%
W 9141
14.6%
F 8847
14.1%
M 8712
13.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 447486
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 71695
16.0%
a 71101
15.9%
y 62554
14.0%
u 35854
8.0%
s 27324
 
6.1%
e 27003
 
6.0%
n 26977
 
6.0%
r 26856
 
6.0%
T 18183
 
4.1%
S 17671
 
3.9%
Other values (7) 62268
13.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 447486
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 71695
16.0%
a 71101
15.9%
y 62554
14.0%
u 35854
8.0%
s 27324
 
6.1%
e 27003
 
6.0%
n 26977
 
6.0%
r 26856
 
6.0%
T 18183
 
4.1%
S 17671
 
3.9%
Other values (7) 62268
13.9%

hour
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.846037
Minimum0
Maximum23
Zeros1368
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size488.8 KiB
2023-03-28T20:05:57.922613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median13
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2670396
Coefficient of variation (CV)0.48785782
Kurtosis-0.89760046
Mean12.846037
Median Absolute Deviation (MAD)5
Skewness-0.23922809
Sum803571
Variance39.275785
MonotonicityNot monotonic
2023-03-28T20:05:58.012758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
13 3319
 
5.3%
18 3265
 
5.2%
14 3252
 
5.2%
12 3223
 
5.2%
11 3217
 
5.1%
19 3204
 
5.1%
15 3202
 
5.1%
20 3117
 
5.0%
10 3097
 
5.0%
9 3062
 
4.9%
Other values (14) 30596
48.9%
ValueCountFrequency (%)
0 1368
2.2%
1 1628
2.6%
2 1632
2.6%
3 1422
2.3%
4 1445
2.3%
5 1485
2.4%
6 1926
3.1%
7 2572
4.1%
8 3013
4.8%
9 3062
4.9%
ValueCountFrequency (%)
23 2306
3.7%
22 2734
4.4%
21 3001
4.8%
20 3117
5.0%
19 3204
5.1%
18 3265
5.2%
17 3031
4.8%
16 3033
4.8%
15 3202
5.1%
14 3252
5.2%

meridiem
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
PM
36687 
AM
25867 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters125108
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAM
2nd rowAM
3rd rowAM
4th rowAM
5th rowAM

Common Values

ValueCountFrequency (%)
PM 36687
58.6%
AM 25867
41.4%

Length

2023-03-28T20:05:58.102978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:58.190477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
pm 36687
58.6%
am 25867
41.4%

Most occurring characters

ValueCountFrequency (%)
M 62554
50.0%
P 36687
29.3%
A 25867
20.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 125108
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 62554
50.0%
P 36687
29.3%
A 25867
20.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 125108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 62554
50.0%
P 36687
29.3%
A 25867
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 125108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 62554
50.0%
P 36687
29.3%
A 25867
20.7%

is_weekend
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size61.2 KiB
False
44883 
True
17671 
ValueCountFrequency (%)
False 44883
71.8%
True 17671
 
28.2%
2023-03-28T20:05:58.272060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

user_id
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size488.8 KiB
user11
37961 
user12
24593 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters375324
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuser11
2nd rowuser11
3rd rowuser11
4th rowuser11
5th rowuser11

Common Values

ValueCountFrequency (%)
user11 37961
60.7%
user12 24593
39.3%

Length

2023-03-28T20:05:58.348444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-28T20:05:58.437233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
user11 37961
60.7%
user12 24593
39.3%

Most occurring characters

ValueCountFrequency (%)
1 100515
26.8%
u 62554
16.7%
s 62554
16.7%
e 62554
16.7%
r 62554
16.7%
2 24593
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 250216
66.7%
Decimal Number 125108
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 62554
25.0%
s 62554
25.0%
e 62554
25.0%
r 62554
25.0%
Decimal Number
ValueCountFrequency (%)
1 100515
80.3%
2 24593
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 250216
66.7%
Common 125108
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 62554
25.0%
s 62554
25.0%
e 62554
25.0%
r 62554
25.0%
Common
ValueCountFrequency (%)
1 100515
80.3%
2 24593
 
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 375324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 100515
26.8%
u 62554
16.7%
s 62554
16.7%
e 62554
16.7%
r 62554
16.7%
2 24593
 
6.6%

Interactions

2023-03-28T20:05:52.835966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:46.865835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.735878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.553123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:49.382385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.337075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.175042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.007360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.936753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:46.987624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.832270image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.655692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:49.484113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.440409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.279050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.109770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:53.039056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.091282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.933264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.768734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:49.595160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.547913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.385373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.214829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:53.138944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.198984image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.042256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.871027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:49.697505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.651827image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.489547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.318309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:53.239988image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.306699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.150374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.973930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:49.798443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.756081image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.594222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.421962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:53.341914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.417247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.250230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:49.077155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.029879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.862533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.699463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.527667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:53.442239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.527280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.350090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:49.180885image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.133760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.968842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.803843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.631845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:53.544634image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:47.637801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:48.450993image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:49.285374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:50.238203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.075194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:51.909323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-28T20:05:52.736817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-28T20:05:58.524987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
actionOptionactionSubOptionemotionPositiveemotionTensionactivitydayday_of_weekhouractionactionSubconditionconditionSub1OptionconditionSub2Optionplacedate_ymdmonthday_namemeridiemis_weekenduser_id
actionOption1.000-0.424-0.015-0.0190.0880.0050.0120.2311.0001.0000.6160.4720.1930.5360.2310.0990.1920.3390.3660.262
actionSubOption-0.4241.000-0.0510.174-0.037-0.047-0.137-0.0590.6650.6650.4960.1670.5160.3520.3660.1920.2280.3160.4800.515
emotionPositive-0.015-0.0511.000-0.309-0.0310.0050.2390.2010.2870.2720.3220.3230.3630.2780.3550.0690.1740.2340.3110.590
emotionTension-0.0190.174-0.3091.000-0.0180.066-0.054-0.0440.3120.2430.3900.3630.4080.3440.2810.1430.1400.0770.1970.596
activity0.088-0.037-0.031-0.0181.0000.013-0.0510.0230.2730.5320.1110.1280.1240.2970.0920.0220.0500.1290.1030.071
day0.005-0.0470.0050.0660.0131.0000.135-0.0290.1530.1430.2560.3340.3410.2331.0001.0000.4850.0610.6940.108
day_of_week0.012-0.1370.239-0.054-0.0510.1351.0000.0230.1870.1410.3290.3910.1470.2521.0000.3601.0000.0611.0000.049
hour0.231-0.0590.201-0.0440.023-0.0290.0231.0000.3530.6250.3970.3230.2310.3750.0640.0460.0451.0000.0680.336
action1.0000.6650.2870.3120.2730.1530.1870.3531.0001.0000.6650.6360.3290.7140.1960.1670.1870.4900.3870.347
actionSub1.0000.6650.2720.2430.5320.1430.1410.6251.0001.0000.5300.3010.1250.9940.1950.0110.1410.3160.1270.098
condition0.6160.4960.3220.3900.1110.2560.3290.3970.6650.5301.0000.6330.1160.7740.4070.1250.3290.1830.4530.119
conditionSub1Option0.4720.1670.3230.3630.1280.3340.3910.3230.6360.3010.6331.0000.1370.5870.5120.1930.3910.1660.7500.209
conditionSub2Option0.1930.5160.3630.4080.1240.3410.1470.2310.3290.1250.1160.1371.0000.2800.4900.0800.1470.1050.1090.745
place0.5360.3520.2780.3440.2970.2330.2520.3750.7140.9940.7740.5870.2801.0000.3610.2180.2520.2290.4850.251
date_ymd0.2310.3660.3550.2810.0921.0001.0000.0640.1960.1950.4070.5120.4900.3611.0001.0001.0000.1091.0000.133
month0.0990.1920.0690.1430.0221.0000.3600.0460.1670.0110.1250.1930.0800.2181.0001.0000.3600.0000.0690.099
day_name0.1920.2280.1740.1400.0500.4851.0000.0450.1870.1410.3290.3910.1470.2521.0000.3601.0000.0611.0000.049
meridiem0.3390.3160.2340.0770.1290.0610.0611.0000.4900.3160.1830.1660.1050.2290.1090.0000.0611.0000.0500.170
is_weekend0.3660.4800.3110.1970.1030.6941.0000.0680.3870.1270.4530.7500.1090.4851.0000.0691.0000.0501.0000.031
user_id0.2620.5150.5900.5960.0710.1080.0490.3360.3470.0980.1190.2090.7450.2510.1330.0990.0490.1700.0311.000

Missing values

2023-03-28T20:05:53.903351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-28T20:05:54.343721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-28T20:05:54.732691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

actionactionOptionactionSubactionSubOptionconditionconditionSub1OptionconditionSub2OptionplaceemotionPositiveemotionTensionactivitydt_kstdate_ymdyearmonthdayday_of_weekday_namehourmeridiemis_weekenduser_id
0sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:00:00+09:002020-08-3020208306Sunday0AMTrueuser11
1sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:01:00+09:002020-08-3020208306Sunday0AMTrueuser11
2sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:02:00+09:002020-08-3020208306Sunday0AMTrueuser11
3sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:03:00+09:002020-08-3020208306Sunday0AMTrueuser11
4sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:04:00+09:002020-08-3020208306Sunday0AMTrueuser11
5sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:05:00+09:002020-08-3020208306Sunday0AMTrueuser11
6sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:06:00+09:002020-08-3020208306Sunday0AMTrueuser11
7sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:07:00+09:002020-08-3020208306Sunday0AMTrueuser11
8sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:08:00+09:002020-08-3020208306Sunday0AMTrueuser11
9sleep111NaNNaNWITH_ONE2.01.0other_indoor4232020-08-30 00:09:00+09:002020-08-3020208306Sunday0AMTrueuser11
actionactionOptionactionSubactionSubOptionconditionconditionSub1OptionconditionSub2OptionplaceemotionPositiveemotionTensionactivitydt_kstdate_ymdyearmonthdayday_of_weekday_namehourmeridiemis_weekenduser_id
62544sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:23:00+09:002020-09-2720209276Sunday23PMTrueuser12
62545sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:24:00+09:002020-09-2720209276Sunday23PMTrueuser12
62546sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:25:00+09:002020-09-2720209276Sunday23PMTrueuser12
62547sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:26:00+09:002020-09-2720209276Sunday23PMTrueuser12
62548sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:27:00+09:002020-09-2720209276Sunday23PMTrueuser12
62549sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:28:00+09:002020-09-2720209276Sunday23PMTrueuser12
62550sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:29:00+09:002020-09-2720209276Sunday23PMTrueuser12
62551sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:30:00+09:002020-09-2720209276Sunday23PMTrueuser12
62552sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:31:00+09:002020-09-2720209276Sunday23PMTrueuser12
62553sleep111NaNNaNALONENaNNaNhome6132020-09-27 23:32:00+09:002020-09-2720209276Sunday23PMTrueuser12